Footnotes
- Salesforce, Sales Teams Using AI 1.3x More Likely to See Revenue Increase, 2024
- PR Newswire, U.S. Data Concerns Soar as AI Surges - 37% of IT Leaders Identify Data Quality as Major Barrier to AI Success, 2024
5 steps to optimize your customer data
The key to unlocking AI’s full potential
The buzz around AI is hard to ignore. According to Salesforce, “83% of sales teams using AI experienced revenue growth compared to 66% without AI1,” which begs the question: "How can we make it work for us?" How can we transform our sales team into AI-powered superstars, effortlessly updating CRM records, generating and scoring leads, all without the burden of manual effort? The secret lies in getting your customer data right – shocking, isn't it?
Historically, attempts to leverage rich customer and prospect data as a way to enable revenue growth has been hindered due to the lack of clean data, analytical capabilities, or the ability to operationalize insights. However, the buzz around GenAI has caused a paradigm shift, encouraging leaders to recraft business processes to capture cost advantages through capabilities like accelerated content creation, access to rapid data-driven insights, and eliminating repetitive tasks.
Not all data is equal, the quality of your data can be as valuable as a Bitcoin or as trivial as a penny; ensuring top-tier data quality is the first step in enabling the next evolution of your business. Today we’ll explore five actions you can take to enable your organization to unlock the full potential of AI.
Step 1. Define "Customer Master Data" (or establish a “Customer Domain”), Audit, and Plan
The first priority in establishing high-quality data to enable your revenue organization is aligning on the definition of “customer master data” across all functions that manage and leverage it including marketing, sales, customer service, and finance. This common understanding ensures that every function of the team interacts with this data set in a consistent manner. Once the organization is aligned, take time to sift through your customer data to identify disparate data sets that impede your ability to create a comprehensive customer master data set. To ensure a thorough assessment, audit distinct sets of customer data independently:
- Account Data: Account name, number, status, geographical footprint, financial data, sales team assignments, order history, service history, NPS, etc.
- Contact Data: Name, title, phone number, email, preferred contact method, interaction history, etc.
In addition to internal data, it’s critical to understand how 3rd-party data sources supplement your customer data. Assess the governance in place, such as the sources of data (e.g., LinkedIn, ZoomInfo) and protocols around seller access restrictions to ensure thorough, untampered data enrichment that seamlessly integrates with your customer master data set.
Keep an eye out for missing data fields, duplicative records, outdated data, and inconsistencies between marketing and sales records, among other gaps. This initial audit provides a clear picture of your data landscape which you can take to IT to properly scope the effort required to improve the quality of your customer data.
Step 2. Clean Up and Create Your Master Data Sets
Once the data cleansing effort has been scoped, partner with IT to execute clean up. This is not a quick fix; expect the process to extend over 12-24 months to fully realize a clean set of customer data. Following clean-up, partner with IT to unify all customer data sources into a customer master data set. This enables effective master data management (MDM), creating a single, reliable source of truth that ensures accurate customer data is integrated with all front-office technologies (e.g., CRM, marketing automation, ERP, CDP) - preventing the misconception that a CRM alone can serve as an MDM solution.
Step 3. Establish Data Governance and Quality Processes and Streamline Front-Office Technology
To minimize discrepancies and improve the ease of maintenance of the customer master data set, standardize data (e.g., Account, Contact and 3rd-party data) collection processes across all touchpoints of the customer journey spanning marketing, sales, and service. Establish standard procedures for data entry, utilizing a consistent format, and establishing data accuracy guidelines. Update CRM and other front-office technology to prompt for required data fields only, leveraging automated data input where applicable. These standardization practices will enhance the reliability of your dataset, allowing you to generate higher-value AI insights.
Step 4. Mandate and Support Adoption
Organizations often underestimate the effort required for business-led CRM adoption and data stewardship. It's a common misstep to rely solely on IT and data teams to maintain data quality and solve all data issues. To make AI work, it's vital that everyone in the organization is bought into front-office technology and understands that effective data governance is driven by the business. Leaders play a crucial role in holding the organization accountable for achieving best-in-class data management. Top-down mandates, along with relevant training and incentives ensure the organization is driving adoption and maintains updated data policies.
Step 5. Monitor and Improve
Customer data cleanup isn't a 'set it and forget it' project; it’s a continuous journey. Keep investing in tools and practices to make data monitoring a regular process, conducting annual audits at a minimum. Proper data governance, maintaining comprehensive dashboards, and establishing a data council are a few ways to guard against outdated information and errors, ensuring your data remains prime and ready to reap the benefits of AI.
In today's rapidly evolving market, the adoption of strong data management practices has become paramount for organizations seeking to thrive in the age of AI. As evidenced from PR Newswire2, “37% of IT leaders identify data quality as major barrier to AI success,” the impact of quality data enabling AI is undeniable. By aligning your data now, you can position yourself to harness the full potential of AI.
If you're eager to learn about aligning your front-office data, sales processes, and sales technology to harness the power of AI, reach out to our team who would be happy to help you navigate the new frontier.
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Walt Becker
Principal, Customer Advisory Leader, Sales Acceleration Leader, KPMG LLP
Susan Hanover
Director, Customer Advisory, KPMG US